Norm Optimal Iterative Learning Control for Planar Tracking Tasks

The Iterative Learning Control (ILC) problem in which tracking is only required at a subset of isolated time points along the trial duration has recently gained significant attention since it addresses the practical needs of many applications. This paper extends the framework by embedding simultaneous iterative convergence of subsets of outputs to reference trajectories on subintervals. This enables it to tackle tasks which mix 'point to point' movements with linear tracking requirements, which substantially broadens the application domain (e.g. to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments). A solution to the problem is presented in the framework of Norm Optimal ILC (NOILC), providing well-defined convergence properties, design guidelines and supporting experimental results